# Arithmetical Binary Decision Tree Traversals

arXiv: https://arxiv.org/abs/2209.04825 · HTML: https://mlsanity.com/paper/2209.04825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 11 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey on Learning to Hash](https://mlsanity.com/paper/1606.00185/) (2016)
- [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017)
- [Optimizing for Interpretability in Deep Neural Networks with Tree Regularization](https://mlsanity.com/paper/1908.05254/) (2019)
- [Transition Matrix Representation of Trees with Transposed Convolutions](https://mlsanity.com/paper/2202.10677/) (2022)

## Abstract (arXiv)

This paper introduces a series of methods for traversing binary decision trees using arithmetic operations. We present a suite of binary tree traversal algorithms that leverage novel representation matrices to flatten the full binary tree structure and embed the aggregated internal node Boolean tests into a single binary vector. Our approach, grounded in maximum inner product search, offers new insights into decision tree.